Exploratory Data Analysis on UKRAINE RUSSIA War (2022)

The EDA on the war will use two datasets.
Important: Each new record is accumulated data from previous days.
Important. Data will be updated daily

We will talk about the following:

1. Tracking
2. Personnel
3. Prisoner of War
4. Armored Personnel Carrier
5. Multiple Rocket Launcher
6. Aircraft
7. Anti-aircraft warfare
8. Drone
9. Field Artillery
10. Fuel Tank
11. Helicopter
12. Military Auto
13. Naval Ship
14. Tank
    

Source of data:source

Acronyms

1. POW - Prisoner of War
   
2. MRL - Multiple Rocket Launcher
   
3. APC - Armored Personnel Carrier
   
4. SRBM - Short-range ballistic missile
   
5. drones: UAV - Unmanned Aerial Vehicle; RPA - Remotely Piloted Vehicle.
   

Import the libraries

import json, requests
import pandas as pd
import numpy as np
import seaborn as sns
import plotly.express as px
import matplotlib.pyplot as plt
import warnings
warnings.filterwarnings('ignore')

Data Preparation and Cleaning

response1 = requests.get('https://raw.githubusercontent.com/PetroIvaniuk/2022-Ukraine-Russia-War-Dataset/main/data/russia_losses_equipment.json')

response1.status_code

200

dict1 = response1.json()

Dataframe for losses on equipments

df1 = pd.DataFrame(dict1)

df1.head()

	date	day	aircraft	helicopter	tank	APC	field artillery	MRL	military auto	fuel tank	drone	naval ship	anti-aircraft warfare	special equipment	mobile SRBM system
0	2022-02-25	2	10	7	80	516	49	4	100	60	0	2	0	NaN	NaN
1	2022-02-26	3	27	26	146	706	49	4	130	60	2	2	0	NaN	NaN
2	2022-02-27	4	27	26	150	706	50	4	130	60	2	2	0	NaN	NaN
3	2022-02-28	5	29	29	150	816	74	21	291	60	3	2	5	NaN	NaN
4	2022-03-01	6	29	29	198	846	77	24	305	60	3	2	7	NaN	NaN

response2 = requests.get('https://raw.githubusercontent.com/PetroIvaniuk/2022-Ukraine-Russia-War-Dataset/main/data/russia_losses_personnel.json')

response2.status_code

200

dict2 = response2.json()

Dataframe for losses on personnel and POW

df2 = pd.DataFrame(dict2)

df2.head()

	date	day	personnel	personnel*	POW
0	2022-02-25	2	2800	about	0
1	2022-02-26	3	4300	about	0
2	2022-02-27	4	4500	about	0
3	2022-02-28	5	5300	about	0
4	2022-03-01	6	5710	about	200

Merge both the dataframes

raw_df = pd.merge(df1,df2)
raw_df.head()

	date	day	aircraft	helicopter	tank	APC	field artillery	MRL	military auto	fuel tank	drone	naval ship	anti-aircraft warfare	special equipment	mobile SRBM system	personnel	personnel*	POW
0	2022-02-25	2	10	7	80	516	49	4	100	60	0	2	0	NaN	NaN	2800	about	0
1	2022-02-26	3	27	26	146	706	49	4	130	60	2	2	0	NaN	NaN	4300	about	0
2	2022-02-27	4	27	26	150	706	50	4	130	60	2	2	0	NaN	NaN	4500	about	0
3	2022-02-28	5	29	29	150	816	74	21	291	60	3	2	5	NaN	NaN	5300	about	0
4	2022-03-01	6	29	29	198	846	77	24	305	60	3	2	7	NaN	NaN	5710	about	200

#Drop the column personnel*

raw_df.drop(['personnel*'],axis = 1,inplace = True)

#Capitalize the first letter of every word for column names

raw_df.columns = [i.title() for i in raw_df.columns]

raw_df.columns

Index(['Date', 'Day', 'Aircraft', 'Helicopter', 'Tank', 'Apc',
       'Field Artillery', 'Mrl', 'Military Auto', 'Fuel Tank', 'Drone',
       'Naval Ship', 'Anti-Aircraft Warfare', 'Special Equipment',
       'Mobile Srbm System', 'Personnel', 'Pow'],
      dtype='object')

#Rename the columns

raw_df = raw_df.rename(columns = {'Field Artillery':'Field_Artillery','Military Auto':'Military_Auto','Fuel Tank':'Fuel_Tank',
                    'Naval Ship':'Naval_Ship','Anti-Aircraft Warfare':'Anti_Aircraft_Warfare','Special Equipment':'Special_Equipment',
                   'Mobile Srbm System':'Mobile_Srbm_System','Apc':'APC','Mrl':'MRL' })

#Calculate the total of equipments lost per day

raw_df['Equipment_Per_Day'] = raw_df[raw_df.columns[2:15]].sum(axis = 1)

raw_df.info()

<class 'pandas.core.frame.DataFrame'>
Int64Index: 43 entries, 0 to 42
Data columns (total 18 columns):
 #   Column                 Non-Null Count  Dtype  
---  ------                 --------------  -----  
 0   Date                   43 non-null     object 
 1   Day                    43 non-null     object 
 2   Aircraft               43 non-null     int64  
 3   Helicopter             43 non-null     int64  
 4   Tank                   43 non-null     int64  
 5   APC                    43 non-null     int64  
 6   Field_Artillery        43 non-null     int64  
 7   MRL                    43 non-null     int64  
 8   Military_Auto          43 non-null     int64  
 9   Fuel_Tank              43 non-null     int64  
 10  Drone                  43 non-null     int64  
 11  Naval_Ship             43 non-null     int64  
 12  Anti_Aircraft_Warfare  43 non-null     int64  
 13  Special_Equipment      25 non-null     float64
 14  Mobile_Srbm_System     16 non-null     float64
 15  Personnel              43 non-null     int64  
 16  Pow                    43 non-null     int64  
 17  Equipment_Per_Day      43 non-null     float64
dtypes: float64(3), int64(13), object(2)
memory usage: 6.4+ KB

#Convert date to datetime

raw_df['Date'] = pd.to_datetime(raw_df['Date'])
raw_df['Day'] = raw_df['Day'].astype(int)

#Sort the null values in descending order

raw_df.isna().sum().sort_values(ascending = False)

Mobile_Srbm_System       27
Special_Equipment        18
Date                      0
Day                       0
Pow                       0
Personnel                 0
Anti_Aircraft_Warfare     0
Naval_Ship                0
Drone                     0
Fuel_Tank                 0
Military_Auto             0
MRL                       0
Field_Artillery           0
APC                       0
Tank                      0
Helicopter                0
Aircraft                  0
Equipment_Per_Day         0
dtype: int64

raw_df['Special_Equipment'].unique()

array([nan, 10., 11., 12., 13., 15., 16., 18., 19., 21., 22., 24., 25.])

raw_df['Mobile_Srbm_System'].unique()

array([nan,  2.,  4.])

#Calculate the percentage of null values in our columns


missing_percentages = raw_df.isna().sum().sort_values(ascending = False)/len(raw_df)*100
missing_percentages

Mobile_Srbm_System       62.790698
Special_Equipment        41.860465
Date                      0.000000
Day                       0.000000
Pow                       0.000000
Personnel                 0.000000
Anti_Aircraft_Warfare     0.000000
Naval_Ship                0.000000
Drone                     0.000000
Fuel_Tank                 0.000000
Military_Auto             0.000000
MRL                       0.000000
Field_Artillery           0.000000
APC                       0.000000
Tank                      0.000000
Helicopter                0.000000
Aircraft                  0.000000
Equipment_Per_Day         0.000000
dtype: float64

# Plot a horizontal bar graph of only null values



percentage_plot = missing_percentages[missing_percentages != 0 ].plot.bar(rot = 0,figsize = (10,10),color = 'lightblue')
plt.ylabel('Missing Values',size = 13)
plt.title(' Missing % of null values for equipments', size = 15)
plt.grid(axis = 'y',linestyle = '--',alpha = 0.8)
plt.bar_label(percentage_plot.containers[0],size = 10)

[Text(0, 0, '62.7907'), Text(0, 0, '41.8605')]

#Check for null rows

null_columns = raw_df[['Mobile_Srbm_System','Special_Equipment']][raw_df.isnull().any(axis = 1)]

We choose to replace null values with zero as data has not been provided for "NaN" values.

raw_df.fillna(0,inplace = True)

raw_df.isnull().any()

Date                     False
Day                      False
Aircraft                 False
Helicopter               False
Tank                     False
APC                      False
Field_Artillery          False
MRL                      False
Military_Auto            False
Fuel_Tank                False
Drone                    False
Naval_Ship               False
Anti_Aircraft_Warfare    False
Special_Equipment        False
Mobile_Srbm_System       False
Personnel                False
Pow                      False
Equipment_Per_Day        False
dtype: bool

#We have no duplicated values

raw_df.duplicated().sum()

0

#Total losses of equipment

col_list = list(raw_df.columns[2:15])

Total = []

for i in raw_df.columns[2:15]:
    Total.append(raw_df[i].sum())


Equipment = pd.DataFrame({'Equipment': col_list,'Total': Total})
Equipment

	Equipment	Total
0	Aircraft	3927.0
1	Helicopter	4201.0
2	Tank	19575.0
3	APC	60021.0
4	Field_Artillery	9149.0
5	MRL	3090.0
6	Military_Auto	36258.0
7	Fuel_Tank	2866.0
8	Drone	1756.0
9	Naval_Ship	188.0
10	Anti_Aircraft_Warfare	1619.0
11	Special_Equipment	493.0
12	Mobile_Srbm_System	62.0

#Total losses in personnel from our raw dataset

print(f'Total personnel losses: {raw_df.Personnel.sum()}')

Total personnel losses: 589150

Note: The dataset conatins a combination of equipments lost for both entities.

Before carrying out any analysis let us cross check our data with real estimates of Russian firepower in comparison with columns in our dataset.

Personnel / Manpower:
Total Military Personnel(est.) : 1,350,000
Total Military Personnel can be broken down into three categories:

1. Active Personnel: 850,000
2. Reserve Personnel: 250,000
3. Paramilitary: 250,000
   
   

Airpower:
Total Aircraft strength:4,173

1. Interceptors (Combat Aircraft): 772
2. Helicopters: 1,543
3. Dedicated Attack (Total Attack aircraft): 739
4. Transports (Fixed wing transport): 445
5. Trainers (Military trainer aircraft): 522
6. Special Mission (Specil mission aircraft): 132
7. Tanker fleet (Tanker fleet aircraft): 20
8. Attack Helicopters (Military attack helicopters): 544
   
   

Land Forces:

1. Tanks : 12,420
2. Armored Vehicles: 30,122
3. Self-propelled artillery: 6,574
4. Towed Artillery: 7,571
5. Rocket Projectors: 3,391
   
   

Naval Forces:

1. Total Assets: 605
2. Aircraft Carriers: 1
3. Destroyers: 15
4. Frigates: 11
5. Corvettes: 86
6. Submarines: 70
7. Patrol Vessels: 59
8. Mine Warfare: 49 source
   
   

NOTE: COMPARE THIS DATA WITH THE TOTAL LOSSES RECORDED IN OUR DATASET

Our losses of helicopters in is far more than the inventory of helicopters in Russia.
The Ukrainian Air Force has: 69 fighters 29 dedicated attack aircraft 32 transports 71 trainers 5 aircraft designated as special mission 112 helicopters source
Combined inventory of helicopters is still less than the losses we have recorded.
The same can be said about the personnel lost.

This is because every row in our dataframe returns the total number of a particular equipment and personnel loss recorded up to that date

SO WHAT TO DO NOW????

IN ORDER TO PROCEED our first step will be to create a revised dataframe that will contain the equipment and personnel lossesfor every 24hrs.

#Assign the list of dates and days to two different variables
date = list(raw_df['Date'])
day = list(raw_df['Day'])

# diff() will be applied to columns in order to analyze the looses every 24hrs


df = raw_df[['Aircraft', 'Helicopter', 'Tank', 'APC',
             'Field_Artillery', 'MRL', 'Military_Auto', 'Fuel_Tank', 'Drone',
             'Naval_Ship', 'Anti_Aircraft_Warfare', 'Special_Equipment',
             'Mobile_Srbm_System', 'Personnel', 'Pow', 'Equipment_Per_Day']].diff()

df['Date'] = date
df['Day'] = day

# Create a dataframe that will contain the values for every 24hrs


df = df.reindex(columns = ['Date', 'Day', 'Aircraft', 'Helicopter', 'Tank', 'APC',
                           'Field_Artillery', 'MRL', 'Military_Auto', 'Fuel_Tank', 'Drone',
                           'Naval_Ship', 'Anti_Aircraft_Warfare', 'Special_Equipment',
                           'Mobile_Srbm_System', 'Personnel', 'Pow', 'Equipment_Per_Day'])
df.head()

	Date	Day	Aircraft	Helicopter	Tank	APC	Field_Artillery	MRL	Military_Auto	Fuel_Tank	Drone	Naval_Ship	Anti_Aircraft_Warfare	Special_Equipment	Mobile_Srbm_System	Personnel	Pow	Equipment_Per_Day
0	2022-02-25	2	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
1	2022-02-26	3	17.0	19.0	66.0	190.0	0.0	0.0	30.0	0.0	2.0	0.0	0.0	0.0	0.0	1500.0	0.0	324.0
2	2022-02-27	4	0.0	0.0	4.0	0.0	1.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	200.0	0.0	5.0
3	2022-02-28	5	2.0	3.0	0.0	110.0	24.0	17.0	161.0	0.0	1.0	0.0	5.0	0.0	0.0	800.0	0.0	323.0
4	2022-03-01	6	0.0	0.0	48.0	30.0	3.0	3.0	14.0	0.0	0.0	0.0	2.0	0.0	0.0	410.0	200.0	100.0

#Save the values in an object
val = raw_df.iloc[0:1,2:18]


#Use the object to replace NaN
df.iloc[0:1,2:18] = val

df.describe()

	Day	Aircraft	Helicopter	Tank	APC	Field_Artillery	MRL	Military_Auto	Fuel_Tank	Drone	Naval_Ship	Anti_Aircraft_Warfare	Special_Equipment	Mobile_Srbm_System	Personnel	Pow	Equipment_Per_Day
count	43.000000	43.000000	43.000000	43.000000	43.000000	43.000000	43.000000	43.000000	43.000000	43.000000	43.000000	43.000000	43.000000	43.000000	43.000000	43.000000	43.000000
mean	24.418605	3.534884	3.186047	16.790698	44.441860	7.953488	2.511628	32.186047	1.767442	2.604651	0.162791	1.279070	0.581395	0.093023	448.837209	10.860465	117.093023
std	13.227375	3.984188	4.811968	16.834712	82.239575	10.823153	3.948309	40.733311	9.226869	3.506046	0.484534	1.816925	1.607017	0.426165	675.207937	32.499510	142.962002
min	2.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	5.000000
25%	13.500000	1.000000	0.000000	7.500000	15.500000	2.000000	0.000000	13.500000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	100.000000	0.000000	60.500000
50%	25.000000	2.000000	2.000000	13.000000	23.000000	5.000000	1.000000	22.000000	0.000000	2.000000	0.000000	1.000000	0.000000	0.000000	200.000000	0.000000	88.000000
75%	35.500000	4.500000	3.000000	16.500000	37.500000	10.000000	3.500000	34.500000	0.000000	3.000000	0.000000	2.000000	1.000000	0.000000	350.000000	9.000000	101.500000
max	46.000000	17.000000	20.000000	80.000000	516.000000	51.000000	17.000000	224.000000	60.000000	15.000000	2.000000	8.000000	10.000000	2.000000	3160.000000	200.000000	828.000000

Check for outliers

def boxplot_plot(x):
    
    for i in df.columns[2:]:
        fig = plt.figure(figsize = (8,8))
        x.boxplot(column = [i])
        plt.grid(False)
        plt.title(i+ ' outliers',fontstyle = 'italic')
        plt.grid(axis = 'y',linestyle = '--',alpha = 0.5)
        plt.tight_layout()
        plt.show()

boxplot_plot(df)

Create a distplot for equipments, personnel and POW

def distplot_plot(x):
    
        for i in df.columns[2:]:
            fig = plt.figure(figsize = (15,5))
    
            sns.distplot(x[i])
    
            mean = x[i].mean()
    
            median = x[i].median()
    
            Q1 =  x[i].quantile(0.25) #25th percentile
    
            Q3 =  x[i].quantile(0.75) #75th percentile
    

    
            plt.axvline(mean,color = 'red',label = 'Mean')
            plt.axvline(median,color = 'green',label = 'Median')
            plt.axvline(Q1,color = 'yellow',label = '25th_ercentile')
            plt.axvline(Q3,color = 'blue',label = '75th_percentile')
            plt.legend()
            plt.title('Distribution of ' + i,fontstyle = 'italic',fontsize = 12) 
            plt.grid(axis = 'y',linestyle = '--',alpha = 0.5)
            plt.show()
        
distplot_plot(df)

We will avoid treating the outliers as any manipultaion of outliers will lead to a significant loss of data for different dates

In our case we need numerical values to accurately analyze the trend for different types of equipments, personnel and POW

Exploratory Analysis and Visualization

#Total losses in personnel till the last date of our dataset

print(f'Total personnel losses after calculating the diff(): {df.Personnel.sum()}')

Total personnel losses after calculating the diff(): 19300.0

#Total losses in equipment till the last date of our dataset

col_list = list(df.columns[2:15])

Total = []

for i in df.columns[2:15]:
    Total.append(df[i].sum())


Equipment_diff= pd.DataFrame({'Equipment_diff': col_list,'Total': Total})
Equipment_diff

	Equipment_diff	Total
0	Aircraft	152.0
1	Helicopter	137.0
2	Tank	722.0
3	APC	1911.0
4	Field_Artillery	342.0
5	MRL	108.0
6	Military_Auto	1384.0
7	Fuel_Tank	76.0
8	Drone	112.0
9	Naval_Ship	7.0
10	Anti_Aircraft_Warfare	55.0
11	Special_Equipment	25.0
12	Mobile_Srbm_System	4.0

On which date the equipments lost was the highest and the lowest?

#Create a line plot

fig = px.line(df,x = 'Date', y = 'Equipment_Per_Day',title = 'Trend in equipments lost')
fig.add_hline(y = df['Equipment_Per_Day'].max(),line_dash="dash", line_color="red",annotation_text="Max. val",opacity = 0.4)
fig.add_hline(y = df['Equipment_Per_Day'].min(),line_dash="dash", line_color="green",annotation_text="Min. val",opacity = 0.4)


#Update trace and layout
fig.update_traces(mode="markers+lines")
fig.update_layout(hovermode="x unified")
fig.show()


# Calculate the date for which the losses in equipments was the highest and the lowest
Max_val = df[['Date','Equipment_Per_Day']][df['Equipment_Per_Day'] == df['Equipment_Per_Day'].max()]
Min_val =  df[['Date','Equipment_Per_Day']][df['Equipment_Per_Day'] == df['Equipment_Per_Day'].min()]


display(Max_val,Min_val)

	Date	Equipment_Per_Day
0	2022-02-25	828.0

	Date	Equipment_Per_Day
2	2022-02-27	5.0

So far the maximum amount of losses have been recorded on 2022-02-25 and the minimum on 2022-02-27. This will be subjected changes that will be dependent on data update.

Which equipment had the maximum and minimum amount of losses?

Equipment with least losses

least_used_equipment = Equipment_diff[Equipment_diff['Total'] == Equipment_diff['Total'].min()]
least_used_equipment

	Equipment_diff	Total
12	Mobile_Srbm_System	4.0

Equipment with min. losses: Mobile Short Range Ballistic Missile (SRBM) System

Equipment with most losses

most_used_equipment = Equipment_diff[Equipment_diff['Total'] == Equipment_diff['Total'].max()]
most_used_equipment

	Equipment_diff	Total
3	APC	1911.0

Equipment with max. losses : APC

fig = px.bar(Equipment,x = 'Equipment',y = 'Total',log_y = True,title = 'Total of each equipment used',height = 700,
            hover_data = ['Equipment','Total'],color = 'Equipment')
fig.show()

Plot for all equipments lost over time

fig = px.bar(df,x = 'Date',y = ['Aircraft', 'Helicopter', 'Tank', 'APC',
                                'Field_Artillery', 'MRL', 'Military_Auto', 'Fuel_Tank', 'Drone',
                                'Naval_Ship', 'Anti_Aircraft_Warfare', 'Special_Equipment',
                                'Mobile_Srbm_System'],log_y = True,
            height= 1000)

fig.update_layout(title = 'Equipments vs Dates',
                 xaxis_title = 'Date',
                 yaxis_title = 'Frequency of Equipments',
                 hovermode="x unified")

fig.show()

Trend for each equipment lost

We create a function that comprises of three different operations:

1. The line plot is to identify the trend for every type of loss for every single day of the ongoing war.
2. The bar plot is to display the percentage drop or rise of losses from their previous day.
3. We also return a dataframe that show us the amount of equipment lost and their respective percentage rise or drop in comparison to the previous day of the war.

def analysis(eq):
    
    #Line plot
    fig = px.line(df,x = 'Date',y = eq ,height = 450)
    
    fig.update_layout(title = eq + ' % loss of over time',
                     xaxis_title = 'Date',
                     yaxis_title = 'Frequency',
                     hovermode="x unified")

    
    fig.add_hline(y=df[eq].max(), line_dash="dot",
              annotation_text="Max. Value", 
              annotation_position="bottom right",
              line_color="green")
                  
                  
    fig.add_hline(y=df[eq].min(), line_dash="dot",
              annotation_text='Min. Value', 
              annotation_position="bottom right",
              line_color="red") 
    
    fig.show()

    print(f' Max.loss: {df[eq].max()}')
    print(f' Min.loss: {df[eq].min()}')
        
    #Create the dataframe
    date = df.Date
    losses = df[eq]


    a = losses.diff()
    df_new = pd.DataFrame({'Date': date,'Loss': losses,'Loss_percentage':a})
    
    df_new['Loss_percentage'] = (df_new['Loss_percentage']/df_new['Loss'])*100
    df_new['Loss_percentage'].replace(np.nan,0,inplace = True)
    df_new['Loss_percentage'].replace(-np.inf,-100,inplace = True) 
    
    #Bar plot
    fig = px.bar(df_new,x = 'Date',y = 'Loss_percentage',color = 'Loss',height = 600,title = '% losses over time')
    fig.update_layout(hovermode="x unified")
    fig.show()
    
    #Return the dataframe
    return df_new 

AIRCRAFTS

Aircraft = analysis('Aircraft')

 Max.loss: 17.0
 Min.loss: 0.0

Aircraft.head()

	Date	Loss	Loss_percentage
0	2022-02-25	10.0	0.000000
1	2022-02-26	17.0	41.176471
2	2022-02-27	0.0	-100.000000
3	2022-02-28	2.0	100.000000
4	2022-03-01	0.0	-100.000000

HELICOPTER

Helicopter = analysis('Helicopter')

 Max.loss: 20.0
 Min.loss: 0.0

Helicopter.head()

	Date	Loss	Loss_percentage
0	2022-02-25	7.0	0.000000
1	2022-02-26	19.0	63.157895
2	2022-02-27	0.0	-100.000000
3	2022-02-28	3.0	100.000000
4	2022-03-01	0.0	-100.000000

TANK

Tank = analysis('Tank')

 Max.loss: 80.0
 Min.loss: 0.0

Tank.head()

	Date	Loss	Loss_percentage
0	2022-02-25	80.0	0.000000
1	2022-02-26	66.0	-21.212121
2	2022-02-27	4.0	-1550.000000
3	2022-02-28	0.0	-100.000000
4	2022-03-01	48.0	100.000000

APC

APC = analysis('APC')

 Max.loss: 516.0
 Min.loss: 0.0

APC.head()

	Date	Loss	Loss_percentage
0	2022-02-25	516.0	0.000000
1	2022-02-26	190.0	-171.578947
2	2022-02-27	0.0	-100.000000
3	2022-02-28	110.0	100.000000
4	2022-03-01	30.0	-266.666667

FIELD ARTILLERY

Field_Artillery= analysis('Field_Artillery')

 Max.loss: 51.0
 Min.loss: 0.0

Field_Artillery.head()

	Date	Loss	Loss_percentage
0	2022-02-25	49.0	0.000000
1	2022-02-26	0.0	-100.000000
2	2022-02-27	1.0	100.000000
3	2022-02-28	24.0	95.833333
4	2022-03-01	3.0	-700.000000

MRL

MRL = analysis('MRL')

 Max.loss: 17.0
 Min.loss: 0.0

MRL.head()

	Date	Loss	Loss_percentage
0	2022-02-25	4.0	0.000000
1	2022-02-26	0.0	-100.000000
2	2022-02-27	0.0	0.000000
3	2022-02-28	17.0	100.000000
4	2022-03-01	3.0	-466.666667

MILITARY AUTO

Military_Auto = analysis('Military_Auto')

 Max.loss: 224.0
 Min.loss: 0.0

Military_Auto.head()

	Date	Loss	Loss_percentage
0	2022-02-25	100.0	0.000000
1	2022-02-26	30.0	-233.333333
2	2022-02-27	0.0	-100.000000
3	2022-02-28	161.0	100.000000
4	2022-03-01	14.0	-1050.000000

FUEL TANK

Fuel_Tank = analysis('Fuel_Tank')

 Max.loss: 60.0
 Min.loss: 0.0

Fuel_Tank.head()

	Date	Loss	Loss_percentage
0	2022-02-25	60.0	0.0
1	2022-02-26	0.0	-100.0
2	2022-02-27	0.0	0.0
3	2022-02-28	0.0	0.0
4	2022-03-01	0.0	0.0

DRONES

Drones = analysis('Drone')

 Max.loss: 15.0
 Min.loss: 0.0

Drones.head()

	Date	Loss	Loss_percentage
0	2022-02-25	0.0	0.0
1	2022-02-26	2.0	100.0
2	2022-02-27	0.0	-100.0
3	2022-02-28	1.0	100.0
4	2022-03-01	0.0	-100.0

NAVAL SHIP

Naval_Ship = analysis('Naval_Ship')

 Max.loss: 2.0
 Min.loss: 0.0

Naval_Ship.head()

	Date	Loss	Loss_percentage
0	2022-02-25	2.0	0.0
1	2022-02-26	0.0	-100.0
2	2022-02-27	0.0	0.0
3	2022-02-28	0.0	0.0
4	2022-03-01	0.0	0.0

ANTI AIRCRAFT WARFARE

Anti_Aircraft_Warfare = analysis('Anti_Aircraft_Warfare')

 Max.loss: 8.0
 Min.loss: 0.0

Anti_Aircraft_Warfare.head()

	Date	Loss	Loss_percentage
0	2022-02-25	0.0	0.0
1	2022-02-26	0.0	0.0
2	2022-02-27	0.0	0.0
3	2022-02-28	5.0	100.0
4	2022-03-01	2.0	-150.0

SPECIAL EQUIPMENT

Special_Equipment = analysis('Special_Equipment')

 Max.loss: 10.0
 Min.loss: 0.0

Special_Equipment.head()

	Date	Loss	Loss_percentage
0	2022-02-25	0.0	0.0
1	2022-02-26	0.0	0.0
2	2022-02-27	0.0	0.0
3	2022-02-28	0.0	0.0
4	2022-03-01	0.0	0.0

MOBILE SRBM SYSTEM

Mobile_SRBM_System = analysis('Mobile_Srbm_System')

 Max.loss: 2.0
 Min.loss: 0.0

Mobile_SRBM_System.head()

	Date	Loss	Loss_percentage
0	2022-02-25	0.0	0.0
1	2022-02-26	0.0	0.0
2	2022-02-27	0.0	0.0
3	2022-02-28	0.0	0.0
4	2022-03-01	0.0	0.0

PERSONNEL

Personnel = analysis('Personnel')

 Max.loss: 3160.0
 Min.loss: 0.0

PRISONER OF WAR

POW = analysis('Pow')

 Max.loss: 200.0
 Min.loss: 0.0

Total Personnel Losses and POW

Personnel_pow_plt = df[['Personnel','Pow']].sum().plot.bar(rot = 0,figsize = (10,10),cmap = 'plasma')
plt.ylabel('Sum',fontsize = 13)
plt.title('Personnel and POW loss',fontsize = 15)
plt.grid(axis = 'y',linestyle = '--',alpha = 0.8)
plt.bar_label(Personnel_pow_plt.containers[0],size = 10)
plt.show()

Aerial eqiupments during war

Create a database for the aerial equipments lost.

Date = df.Date
Aircraft =  df.Aircraft
Helicopter = df.Helicopter
Drone = df.Drone
Anti_Aircraft_Warfare = df.Anti_Aircraft_Warfare
Mobile_Srbm_System = df.Mobile_Srbm_System

df_aerial = pd.DataFrame({'Date': Date,'Aircaft':Aircraft,'Helicopter':Helicopter,'Drone':Drone,
                          'Anti_Aircraft_Warfare':Anti_Aircraft_Warfare,'Mobile_Srbm_System':Mobile_Srbm_System})
df_aerial['Total_Losses_Aerial'] = df_aerial[df_aerial.columns[1:6]].sum(axis = 1) 
df_aerial.head()

	Date	Aircaft	Helicopter	Drone	Anti_Aircraft_Warfare	Mobile_Srbm_System	Total_Losses_Aerial
0	2022-02-25	10.0	7.0	0.0	0.0	0.0	17.0
1	2022-02-26	17.0	19.0	2.0	0.0	0.0	38.0
2	2022-02-27	0.0	0.0	0.0	0.0	0.0	0.0
3	2022-02-28	2.0	3.0	1.0	5.0	0.0	11.0
4	2022-03-01	0.0	0.0	0.0	2.0	0.0	2.0

We will create a new dataframe from our dataframe of losses for aerial equipments.
This dataframe will help us see the contribution of different aerial equipments (percentage) towards the total of aerial equipments lost

col_list = list(df_aerial.columns[1:6])

Percentage = []

for i in df_aerial.columns[1:6]:
    Percentage.append(round((df_aerial[i].sum()/df_aerial['Total_Losses_Aerial'].sum())*100,1))

df_aerial_percentage = pd.DataFrame({'Aerial Equipment': col_list, '%_of_sum_loss_aerial':Percentage})
df_aerial_percentage

	Aerial Equipment	%_of_sum_loss_aerial
0	Aircaft	33.0
1	Helicopter	29.8
2	Drone	24.3
3	Anti_Aircraft_Warfare	12.0
4	Mobile_Srbm_System	0.9

fig = px.pie(df_aerial_percentage, values='%_of_sum_loss_aerial', 
             names='Aerial Equipment', color_discrete_sequence=px.colors.sequential.Blues)
fig.show()

Ground equipments during war

Create a database for the ground equipments lost.

Date = df.Date
Tank = df.Tank
APC = df.APC
Field_Artillery = df.Field_Artillery
MRL = df.MRL
Military_Auto = df.Military_Auto
Fuel_Tank = df.Fuel_Tank

df_ground = pd.DataFrame({'Date':Date,'Tank': Tank,'APC': APC,'Field_Artillery':Field_Artillery,
                          'MRL':MRL,'Military_Auto':Military_Auto,'Fuel_Tank':Fuel_Tank})
df_ground['Total_Losses_Ground'] =df_ground[df_ground.columns[1:7]].sum(axis = 1)
df_ground.head()

	Date	Tank	APC	Field_Artillery	MRL	Military_Auto	Fuel_Tank	Total_Losses_Ground
0	2022-02-25	80.0	516.0	49.0	4.0	100.0	60.0	809.0
1	2022-02-26	66.0	190.0	0.0	0.0	30.0	0.0	286.0
2	2022-02-27	4.0	0.0	1.0	0.0	0.0	0.0	5.0
3	2022-02-28	0.0	110.0	24.0	17.0	161.0	0.0	312.0
4	2022-03-01	48.0	30.0	3.0	3.0	14.0	0.0	98.0

We will create a new dataframe from our dataframe of losses for ground equipments.
This dataframe will help us see the contribution of different ground equipments (percentage) towards the total of ground equipments lost

col_list = list(df_ground.columns[1:7])

Percentage = []

for i in df_ground.columns[1:7]:
    Percentage.append(round((df_ground[i].sum()/df_ground['Total_Losses_Ground'].sum())*100,1))

df_ground_percentage = pd.DataFrame({'Ground Equipment': col_list, '%_of_sum_loss_ground':Percentage})
df_ground_percentage

	Ground Equipment	%_of_sum_loss_ground
0	Tank	15.9
1	APC	42.1
2	Field_Artillery	7.5
3	MRL	2.4
4	Military_Auto	30.5
5	Fuel_Tank	1.7

fig = px.pie(df_ground_percentage, values='%_of_sum_loss_ground', 
             names='Ground Equipment', color_discrete_sequence=px.colors.sequential.RdBu)
fig.show()

Naval equipments during war

Create a database for the naval equipments lost.

Date = df.Date
Naval = df.Naval_Ship

df_naval = pd.DataFrame({'Date': Date,'Naval': Naval})
df_naval.head()

	Date	Naval
0	2022-02-25	2.0
1	2022-02-26	0.0
2	2022-02-27	0.0
3	2022-02-28	0.0
4	2022-03-01	0.0

#Plot a line plot to analyze ground, aerial and naval equipments lost

fig = px.line(x = df.Date, y = [df_aerial.Total_Losses_Aerial,df_ground.Total_Losses_Ground,df_naval.Naval])
names={'wide_variable_0':'Aerial Equipments','wide_variable_1':'Ground Equipments ','wide_variable_2':'Naval Equipments'}
fig.for_each_trace(lambda x: x.update(name = names[x.name]))
fig.update_layout(hovermode="x unified",yaxis_title = 'Frequency',title = 'Aerial Equipments vs Ground Equipments vs Naval Equipments')
fig.show()

The war signals towards a heavy loss of ground equipments. Whereas the use of naval equipments is almost null. Let us calculate the total for each category.

df_eq = pd.DataFrame()
df_eq['Total_Naval_Losses'] = df_naval.Naval
df_eq['Total_Ground_losses'] = df_ground.Total_Losses_Ground
df_eq['Total_Aerial_Losses'] = df_aerial.Total_Losses_Aerial
eq_plt = df_eq[['Total_Naval_Losses','Total_Ground_losses','Total_Aerial_Losses']].sum().plot.bar(rot = 0,figsize = (10,10),cmap = 'summer')
plt.ylabel('Sum',fontsize = 13)
plt.title('Sum for categories of equipment',fontsize = 15)
plt.grid(axis = 'y',linestyle = '--',alpha = 0.8)
plt.bar_label(eq_plt.containers[0],size = 10)
plt.semilogy()
plt.show()

We can see that the amount of losses of ground equipment is far more than the losses in other categories till now.